code: def save(prefix, predicted):
      for key, value in predicted.items():
          filename = os.path.splitext(key)[0]
          out_file = filename + "_pred"
          np.save(os.path.join(prefix, out_file), value)
  
file_path: torch-points3d/forward_scripts/forward.py
imports: import torch
  import hydra
  import logging
  from omegaconf import OmegaConf
  import os
  import sys
  import numpy
  from typing import Dict
  from torch_points3d.datasets.dataset_factory import instantiate_dataset, get_dataset_class
  from torch_points3d.models.model_factory import instantiate_model
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.datasets.base_dataset import BaseDataset
  from torch_points3d.metrics.colored_tqdm import Coloredtqdm
  from torch_points3d.metrics.model_checkpoint import ModelCheckpoint
  from torch_points3d.utils.colors import COLORS
label_desc: Use image node_id 1 for calling a global function with example usage: save(prefix, predicted) without return types